Course Plan

Credits: 4

Instructor:

    • Dr. Richa Singh
    • Class Hours: 

Pre-requisites:

  • Good programming
  • Probability and Statistics, Linear Algebra
  • Pattern Recognition (strongly recommended)
Post Condition:

    • Understand concepts of machine learning and some techniques in detail
    • Enable them to understand advance concept/technique on their own
    • Understand the challenge that lies with AI and machine learning techniques
    • Capability to model any problem using appropriate machine learning techniques

Topics to be Covered:

    • Concept learning
    • Instance based learning
    • Decision trees
    • Random decision forest 
    • Neural network
    • Kernel machines
    • Unsupervised learning and regression
    • Reinforcement learning
    • Genetic algorithms
    • Evolutionary algorithms
Evaluation Scheme:

    • Assignments, paper reading and presentation: 30%
    • Exams: 35%
    • Quizzes: 10%
    • Project: 25%
Textbook:

    • Tom Mitchell, Machine Learning